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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and

Marjan Kordani1, Mohsen Bagheritabar2, Iman Ahmadianfar3,4

  • 1Department of Hydrology and Water Resources, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

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Summary
This summary is machine-generated.

Forecasting irrigation water quality indicators like Permeability Index (PI) and Magnesium Absorption Ratio (MAR) is crucial for agriculture. This study introduces a novel hybrid model (OMVMD-GRKR) that accurately predicts these indices, outperforming existing methods.

Keywords:
Generalized ridge regressionKernel ridge regressionMagnesium absorption ratioOptimized MVMDPermeability indexWater quality

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Machine Learning Applications

Background:

  • Irrigation water quality indicators (IWQI), such as Permeability Index (PI) and Magnesium Absorption Ratio (MAR), are vital for assessing agricultural water suitability.
  • Accurate forecasting of IWQI parameters is challenging due to complex time-series data and limited input sequences.
  • The Karun River in Iran is a critical water source, necessitating reliable methods for monitoring its quality for irrigation.

Purpose of the Study:

  • To develop an innovative hybrid intelligence framework for forecasting PI and MAR indices.
  • To address the complexity of IWQI prediction using time-series data.
  • To establish a reliable method for evaluating agricultural water supplies in the Karun River basin.

Main Methods:

  • A novel hybrid machine learning (ML) model, generalized ridge regression and kernel ridge regression with a regularized locally weighted (GRKR) method, was developed.
  • An optimized multivariate variational mode decomposition (OMVMD) technique, optimized by the Runge-Kutta algorithm (RUN), was used for input variable decomposition.
  • The light gradient boosting machine model (LGBM) was employed for selecting influential input variables, and the GRKR model was coupled with OMVMD.

Main Results:

  • The proposed OMVMD-GRKR model demonstrated superior performance in forecasting PI and MAR indices at both Ahvaz and Molasani stations.
  • Statistical metrics showed high accuracy: R=0.987, RMSE=0.761 for Ahvaz, and R=0.963, RMSE=1.379 for Molasani.
  • The OMVMD-GRKR model significantly outperformed other methods, including OMVMD, Ridge, LSSVM, DRVFL, and DELM.

Conclusions:

  • The OMVMD-GRKR framework provides a highly effective and accurate approach for predicting irrigation water quality indicators.
  • This novel hybrid model offers a valuable tool for water resource management and ensuring agricultural sustainability.
  • The study highlights the potential of advanced hybrid ML techniques in addressing complex environmental forecasting challenges.